library(tidyverse)
library(openintro)
library(maps)
library(ggmap)
library(gplots)
library(RColorBrewer)
library(sf)
library(leaflet)
library(ggthemes)
library(gganimate)
library(transformr)
library(gifski)
drought <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-07-20/drought.csv')
'%!in%' <- Negate('%in%')
What does the drought severity in each state look like overtime?
drought_severity <- drought %>%
mutate(severity = recode(drought_lvl, "None" = 0, "D0" = 1, "D1" = 2, "D2" = 3, "D3" = 4, "D4" = 5),
severity = severity * (area_pct/100)) %>%
group_by(state_abb, valid_start) %>%
summarize(week_severity = sum(severity)) %>%
mutate(state = abbr2state(state_abb),
state = str_to_lower(state))
states_map <- map_data("state")
drought_sev_map <- drought_severity %>%
filter(!is.na(state), !is.na(week_severity)) %>%
filter(valid_start %in% seq.Date(as.Date("2001-07-17"), as.Date("2021-07-13"), by = 4)) %>%
ggplot() +
geom_map(map = states_map,
aes(map_id = state, group = valid_start,
fill = week_severity)) +
expand_limits(x = states_map$long, y = states_map$lat) +
scale_fill_viridis_c(option = "E")+
labs(title = "Drought Severity in the United States Overtime: {closest_state}", fill = "", subtitle = "Severity calculated using the drought level and the percent of the state impacted by drought", caption = "Plot created by Erin Franke, data from Drought Monitor") +
theme_map() +
theme(plot.title = element_text(face = "bold", size = 12, color = "navy"))+
coord_fixed(ratio = 1.3)+
transition_states(valid_start)
animate(drought_sev_map, nframes = 560, end_pause = 10)
anim_save("drought_sev_map.gif")
knitr::include_graphics("drought_sev_map.gif")